CheckMate: LLM-Powered Approximate Intermittent Computing
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929733456887808 |
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| author | Sayyid-Ali, Abdur-Rahman Ibrahim Rafay, Abdul Soomro, Muhammad Abdullah Alizai, Muhammad Hamad Bhatti, Naveed Anwar |
| author_facet | Sayyid-Ali, Abdur-Rahman Ibrahim Rafay, Abdul Soomro, Muhammad Abdullah Alizai, Muhammad Hamad Bhatti, Naveed Anwar |
| contents | Batteryless IoT systems face energy constraints exacerbated by checkpointing overhead. Approximate computing offers solutions but demands manual expertise, limiting scalability. This paper presents CheckMate, an automated framework leveraging LLMs for context-aware code approximations. CheckMate integrates validation of LLM-generated approximations to ensure correct execution and employs Bayesian optimization to fine-tune approximation parameters autonomously, eliminating the need for developer input. Tested across six IoT applications, it reduces power cycles by up to 60% with an accuracy loss of just 8%, outperforming semi-automated tools like ACCEPT in speedup and accuracy. CheckMate's results establish it as a robust, user-friendly tool and a foundational step toward automated approximation frameworks for intermittent computing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17732 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CheckMate: LLM-Powered Approximate Intermittent Computing Sayyid-Ali, Abdur-Rahman Ibrahim Rafay, Abdul Soomro, Muhammad Abdullah Alizai, Muhammad Hamad Bhatti, Naveed Anwar Distributed, Parallel, and Cluster Computing Batteryless IoT systems face energy constraints exacerbated by checkpointing overhead. Approximate computing offers solutions but demands manual expertise, limiting scalability. This paper presents CheckMate, an automated framework leveraging LLMs for context-aware code approximations. CheckMate integrates validation of LLM-generated approximations to ensure correct execution and employs Bayesian optimization to fine-tune approximation parameters autonomously, eliminating the need for developer input. Tested across six IoT applications, it reduces power cycles by up to 60% with an accuracy loss of just 8%, outperforming semi-automated tools like ACCEPT in speedup and accuracy. CheckMate's results establish it as a robust, user-friendly tool and a foundational step toward automated approximation frameworks for intermittent computing. |
| title | CheckMate: LLM-Powered Approximate Intermittent Computing |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2411.17732 |